54 citations · 239 across the 30 of their papers we have counts for
32 papers
Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities
Nick Lemke, Niklas Ihm, John Kalkhof +11
Neural Cellular Automata (NCAs) are a new type of neural network architecture which enable accurate and robust inference at extremely small model sizes. Recently, NCAs have advance…
Atlas of MICCAI: 28 Years of Representation, Collaboration, and the Road to Inclusion
Amelia Jiménez-Sánchez, Laura Arbelaez Ossa, Joan Perramon-Llussà +6
Scientific knowledge in medical image analysis is shaped by venues such as the Medical Image Computing and Computer-Assisted Intervention (MICCAI) conference, which influence resea…
Dense Temporal Contrast Synthesis via Conditioned Latent Transport
Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang +15
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in…
Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
Johannes Kiechle, Richard Osuala, Daniel M. Lang +5
In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated…
Regulatory Approval Is Not Enough: Gaps in Trustworthy AI Reporting in FDA-Cleared Medical Devices
Ahmed M Salih, Oliver Díaz, Alejandro Guzman +6
Background: AI/ML-enabled medical devices are increasingly deployed in healthcare under evolving regulatory frameworks. As these systems become more integrated into clinical decisi…
A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy +5
Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition b…
Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI
Joan Perramon-Llussà, Amelia Jiménez-Sánchez, Grzegorz Skorupko +4
Foundation models (FMs) show great promise for robust downstream performance across medical imaging tasks and modalities, including cardiac magnetic resonance (CMR), following task…
The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
Lidia Garrucho, Smriti Joshi, Kaisar Kushibar +43
Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enhanced magnetic resonance imagin…
Agent-Based Output Drift Detection for Breast Cancer Response Prediction in a Multisite Clinical Decision Support System
Xavier Rafael-Palou, Jose Munuera, Ana Jimenez-Pastor +3
Modern clinical decision support systems can concurrently serve multiple, independent medical imaging institutions, but their predictive performance may degrade across sites due to…
Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation
Stefan M. Fischer, Johannes Kiechle, Laura Daza +6
In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the…
Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Images
Sebastian Ibarra, Javier del Riego, Alessandro Catanese +7
Dynamic contrast-enhanced (DCE) MRI is essential for breast cancer diagnosis and treatment. However, its reliance on contrast agents introduces safety concerns, contraindications,…
Mask-Guided Multi-Channel SwinUNETR Framework for Robust MRI Classification
Smriti Joshi, Lidia Garrucho, Richard Osuala +2
Breast cancer is one of the leading causes of cancer-related mortality in women, and early detection is essential for improving outcomes. Magnetic resonance imaging (MRI) is a high…
Clinically-guided Data Synthesis for Laryngeal Lesion Detection
Chiara Baldini, Kaisar Kushibar, Richard Osuala +4
Although computer-aided diagnosis (CADx) and detection (CADe) systems have made significant progress in various medical domains, their application is still limited in specialized f…
Single Image Test-Time Adaptation via Multi-View Co-Training
Smriti Joshi, Richard Osuala, Lidia Garrucho +4
Test-time adaptation enables a trained model to adjust to a new domain during inference, making it particularly valuable in clinical settings where such on-the-fly adaptation is re…
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas +29
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key…
Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging
Isabella Cama, Alejandro Guzmán, Cristina Campi +4
Most papers caution against using predictive models for disease stratification based on unselected radiomic features, as these features are affected by contouring variability. Inst…
Federated nnU-Net for Privacy-Preserving Medical Image Segmentation
Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla +11
The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, a…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou +6
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition.…
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Richard Osuala, Daniel M. Lang, Anneliese Riess +6
Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
Mitigating annotation shift in cancer classification using single image generative models
Marta Buetas Arcas, Richard Osuala, Karim Lekadir +1
Artificial Intelligence (AI) has emerged as a valuable tool for assisting radiologists in breast cancer detection and diagnosis. However, the success of AI applications in this dom…
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Richard Osuala, Daniel M. Lang, Preeti Verma +9
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization…
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou +4
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, incl…